class_docstr: Bbox sampling result.
  
  Example:
      >>> # xdoctest: +IGNORE_WANT
      >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
      >>> self = SamplingResult.random(rng=10)
      >>> print(f'self = {self}')
      self = <SamplingResult({
          'neg_bboxes': torch.Size([12, 4]),
          'neg_inds': tensor([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
          'num_gts': 4,
          'pos_assigned_gt_inds': tensor([], dtype=torch.int64),
          'pos_bboxes': torch.Size([0, 4]),
          'pos_inds': tensor([], dtype=torch.int64),
          'pos_is_gt': tensor([], dtype=torch.uint8)
      })>
code: def __nice__(self):
      data = self.info.copy()
      data["pos_bboxes"] = data.pop("pos_bboxes").shape
      data["neg_bboxes"] = data.pop("neg_bboxes").shape
      parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
      body = "    " + ",\n    ".join(parts)
      return "{\n" + body + "\n}"
  
file_path: insightface/detection/scrfd/mmdet/core/bbox/samplers/sampling_result.py
imports: import torch
  from mmdet.utils import util_mixins
label_desc: Use image node_id 4 for calling the SamplingResult obj's underlying member method code with example usage: obj.__nice__() and returns: unknown